Merge branch 'master' of https://zdv2.bktei.com/gitweb/baltakatei-exdev

[BK-2020-03.git] / unitproc / python / sleepRand.py

diff --git a/unitproc/python/sleepRand.py b/unitproc/python/sleepRand.py
index 76a32e10172aa696e911e11a068817a3fe9d6c33..7a9b2b74e2674d115a34225b6860db637dcfd61b 100755 (executable)
--- a/unitproc/python/sleepRand.py
+++ b/unitproc/python/sleepRand.py
@@ -1,17 +1,59 @@
 #!/usr/bin/env python3
 # Desc: Pauses a random amount of time. Random distribution is inverse gaussian.
 #!/usr/bin/env python3
 # Desc: Pauses a random amount of time. Random distribution is inverse gaussian.

-# Version: 0.0.1
+# Version: 0.0.5

 # Depends: python 3.7.3
 # Depends: python 3.7.3

-# Usage: ./sleepRand.py arg1
-# Input: arg1: float seconds (mean of inverse gaussian distribution)
-# Example: python3 sleepRand.py 4.0
+# Usage: ./sleepRand.py [-v] [-p P] SECONDS
+# Input: SECONDS: float seconds (mean of inverse gaussian distribution)
+#        P:       precision (lambda of inverse gaussian distribution)
+# Example: python3 sleepRand.py -vv -p 8.0 60.0

 
 

-import math, time, random, sys
+import argparse;
+import math, time, random, sys;
+import logging;
+
+# Set up argument parser (see https://docs.python.org/3.7/library/argparse.html )
+parser = argparse.ArgumentParser(
+    description='Delay activity for a random number of seconds. Delays sampled from an inverse gaussian distribution.',
+    epilog="Author: Steven Baltakatei Sandoval. License: GPLv3+");
+parser.add_argument('-v','--verbose',
+                    action='count',
+                    dest='verbosity',
+                    default=0,
+                    help='Verbose output. (repeat for increased verbosity)');
+parser.add_argument('mean',
+                    action='store',
+                    metavar='SECONDS',
+                    nargs=1,
+                    default=1,
+                    type=float,
+                    help='Mean seconds of delay. Is the mean of the inverse gaussian distribution.');
+parser.add_argument('--precision','-p',
+                    action='store',
+                    metavar='P',
+                    nargs=1,
+                    default=[4.0],
+                    type=float,
+                    help='How concentrated delays are around the mean (default: 4.0). Must be a positive integer or floating point value. Is the lambda factor in the inverse gaussian distribution. High values (e.g. > 10.0) cause random delays to rarely stray far from MEAN. Small values (e.g. < 0.10) result in many small delays plus occasional long delays.');
+args = parser.parse_args();

 
 # Define functions
 
 # Define functions

+def setup_logging(verbosity):
+    '''Sets up logging'''
+    # Depends: module: argparse
+    # Ref/Attrib: Haas, Florian; Configure logging with argparse; https://xahteiwi.eu/resources/hints-and-kinks/python-cli-logging-options/
+    base_loglevel = 30;
+    verbosity = min(verbosity, 2);
+    loglevel = base_loglevel - (verbosity * 10);
+    logging.basicConfig(level=loglevel,
+                        format='%(message)s');
+

 def randInvGau(mu, lam):
     """Returns random variate of inverse gaussian distribution"""
 def randInvGau(mu, lam):
     """Returns random variate of inverse gaussian distribution"""

-    # Ref/Attrib: doi:10.1080/00031305.1976.10479147
+    # input: mu:  mean of inverse gaussian distribution
+    #        lam: shape parameter
+    # output: float sampled from inv. gaus. with range 0 to infinity, mean mu
+    # example: sample = float(randInvGau(1.0,4.0));
+    # Ref/Attrib: Michael, John R. "Generating Random Variates Using Transformations with Multiple Roots" https://doi.org/10.2307/2683801

     nu = random.gauss(0,1);
     y = nu ** 2;
     xTerm1 = mu;
     nu = random.gauss(0,1);
     y = nu ** 2;
     xTerm1 = mu;


@@ -24,18 +66,36 @@ def randInvGau(mu, lam):
     else:
         return (mu ** 2 / x);
 
     else:
         return (mu ** 2 / x);
 

-# Check input (TODO)
-arg1 = float(sys.argv[1]); # first argument
-desMean = arg1;
+# Process input
+## Start up logger
+setup_logging(args.verbosity);
+logging.debug('DEBUG:Debug logging output enabled.');
+logging.debug('DEBUG:args.verbosity:' + str(args.verbosity));
+logging.debug('DEBUG:args:' + str(args));

 
 

-# Configure
-lambdaFactor = 4; # spread factor; inversely proportional to variance
+## Reject negative floats.
+try:
+    ### Get desired mean
+    desMean = args.mean[0];    
+    logging.debug('DEBUG:Desired mean:' + str(desMean));
+    ### Get lambda precision factor
+    lambdaFactor = args.precision[0];
+    logging.debug('DEBUG:Lambda precision factor:' + str(lambdaFactor));
+    if desMean < 0:
+        logging.error('ERROR:Desired mean is negative:' + str(desMean));
+        raise ValueError('Negative number error.');
+    if lambdaFactor < 0:
+        logging.error('ERROR:Lambda precision factor is negative:' + str(lambdaFactor));
+        raise ValueError('Negative number error.');
+except ValueError:
+    sys.exit(1);

 
 # Calculate delay
 delay = randInvGau(desMean, desMean * lambdaFactor);
 
 # Calculate delay
 delay = randInvGau(desMean, desMean * lambdaFactor);

-#print('DEBUG:delay:' + str(float(delay)));
+logging.debug('delay:' + str(delay));

 
 # Sleep
 time.sleep(float(delay));
 
 
 # Sleep
 time.sleep(float(delay));
 

-
+# Author: Steven Baltakatei Sandoal
+# License: GPLv3+